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Models for audio source separation usually operate on the magnitude spectrum, which ignores phase information and makes separation performance dependant on hyper-parameters for the spectral front-end.
#9 best model for Music Source Separation on MUSDB18
The thud of a bouncing ball, the onset of speech as lips open -- when visual and audio events occur together, it suggests that there might be a common, underlying event that produced both signals.
Based on this idea, we drive the separator towards outputs deemed as realistic by discriminator networks that are trained to tell apart real from separator samples.
This paper deals with the problem of audio source separation.
The input vector is embedded to obtain the parameters that control Feature-wise Linear Modulation (FiLM) layers.
As a result, source separation GP models have been restricted to the analysis of short audio frames.
We apply our method to image generation, image segmentation and audio source separation, and obtain improved performance over a standard GAN when additional incomplete training examples are available.